As illustrated in Fig.REF b, we stack cycles in sequence to construct a deep network model.
We add a standard convolution as a first layer of the network, to obtain the appropriate number of features to be used in the first cycle, and a dense layer as the last layer, thus reducing the (flattened) tensor to the size of the final output.
In cubic models, tensor width is changed from one cycle to the next using tri-linear interpolation.
In non-cubic models, tensor shape is changed by controlling the output size of the fully connected coordinate at each layer.
In the case of the Pathfinder challenge, we use global pooling before the dense layer, in order to match the baseline architectures of [1]}.
Code will be made available to the reviewers in the supplementary material, and to the public upon acceptance of the paper.
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